Various industries, such as the healthcare industry, may churn out an enormous amount of data related to the various stakeholders of the industry. Analysing such enormous data to draw meaningful trends and insights therefrom may be an important task for various players of the industry for deriving competitive advantage. Various mathematical models may be used to identify trends and categorize the data into well-defined categories. For instance, the healthcare industry may maintain various records of human subjects/patients such as, but not limited to, medical diagnosis records, medical insurance records, hospital data, etc. Based on one or more mathematical models, the records of the human subjects/patients may be classified into various categories such as health conditions of human subjects/patients, health insurance fraud risks, and so on.
Typically, the data, which is to be analysed, may include fields of various types. For example, the medical records may include various fields of numerical data type, for instance, BP measure, heart rate, and blood sugar measure. Further, the medical records may also include various fields of categorical data type, for example, gender. The mathematical models used to analyse such records may only consider the data of numerical data type to identify the trends and categorize them. Further, analysis of records having a large number of fields may as such be a cumbersome task.